ABSTRACT A fundamental question in audition is how complex sounds are accurately recognized despite wide variations in listening conditions. Accurate and robust recognition is especially important for behaviorally critical sounds such as speech or animal vocalizations (calls). Large individual differences in the production of speech or calls, and environmental distortions due to reverberations and noise, result in considerable variability in the acoustics of calls when they arrive at the ear. A critical function of the auditory system is to group these diverse signals into functional categories, such as a call type in animal communication, or words in human speech. In this proposal, we will begin to elucidate the mechanisms by which invariant categorization of calls is accomplished in auditory cortex. In Aim 1, we will first develop and validate a model for how neural circuits might achieve call categorization while remaining invariant to production variability. This model makes specific predictions about the tuning properties of single neurons and neural populations in auditory cortex. We will test these predictions experimentally using in-vivo recordings in awake Guinea pigs. Preliminary data suggests that neurons in superficial cortical layers (L2/3) of primary auditory cortex (A1) encode ?mid-level? features that underlie production-invariant call categorization. In Aim 2, we will test the hypothesis that both production-invariant selectivity to specific call types, as well as a high degree of invariance to listening conditions co-emerge in A1 L2/3. We will analyze the responses of single neurons as well as neural populations in thalamus, A1 layer 4 (L4), and A1 L2/3 to call stimuli presented in ideal and systematically degraded listening conditions. Preliminary data suggest that A1 L2/3 neurons, but not thalamic neurons, are able to maintain their feature selectivity in a wide range of listening conditions. Finally, in Aim 3, we will test the hypothesis that cortical inhibition is crucial for generating invariance to listening conditions, but not call selectivity. Specifically, we will use optogenetic approaches with novel viral vectors to characterize the effects of decreased inhibition on selectivity and invariance in A1 single neurons and the A1 population. Together, the theoretical model, and behavioral, electrophysiological, and optogenetic data will give rise to a general computational principle by which the auditory cortex extracts useful features from noisy inputs. Insights gained from these experiments will provide a novel framework for understanding auditory processing in normal circuits, as well as during circuit dysfunction in communication disorders such as autism, dyslexia and specific language impairment.